Artificial Intelligence
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Artificial Intelligence A Quick Start Guide Artificial Intelligence is easily one of the most hyped subjects in the world right now. It is the repository of much hope as well as the anxiety we as a society feel about the future – will it prove to be the source of immeasurable wealth and progress for much of society, or will the few reap its rewards? Will it destroy more jobs than it creates? Or will it go all Skynet on us?! Artificial Intelligence is here today and it’s neither magic nor alchemy. Nor even a mystery. With a little effort, you can get a decent understanding on what it is, and more importantly, how you can apply it to your business. As our Senior Data Scientist Daryl Weir highlights in his wonder- ful article, AI is always a computer program someone wrote. One of the goals in the work we do in this area is to demystify AI and increase people’s understanding of what it is and what it can do. We believe this is the best way to make sure AI is used in a way that benefits society as a whole. What is Artificial Intelligence? The dictionary definition is: The theory and development of computer systems able to perform tasks normally requiring human intel- ligence, such as visual perception, speech recognition, decision-making, and translation between languages 3 | Why is AI so hot right now? It all boils down to the convergence of three distinct phenomena: 01 the amount of data is increasing exponentially, 02 computing power is doing the same and cloud computing is cheap, and 03 as a result of items 1 and 2, neural networks have become useful. The AI hype cycle is cresting, buoyed by a lot of hot air. The bal- loon will soon lose altitude, just like the Internet balloon did after its initial stratospheric lift. And just like the Internet ended up Artificial Intelligence Machine Learning Deep learning | 4 changing our lives in ways much more profound than envisioned during the first hype, AI will bounce back and have a transforma- tive impact on us as a species. So it pays to stay on board. Why does any of this matter to you or your business? How is it any different from other recent business buzzwords like ‘digitalization’ or from the meat-and-potatoes work of software development in general? These are some of the questions we look at in this collection of articles from our experts. Over the last few years, we have worked on a wide variety of cas- es that involve AI to varying degrees, with clients like Supercell, Barona, ABB, Veikkaus, Sanoma, as well as a number of NGOs, etc. Our work includes solutions for recruitment, hate speech detection, customer support, personalisation, facial recognition, and many others. In this little pamphlet we’ve collected some of the articles our em- ployees have written about Machine Learning and AI. They delve into what ML and AI are, how machines actually learn, how AI/ ML differ from software development – which many of you may already be quite familiar with – and, for inspiration, how data can be monetized. We hope you’ll find it an enjoyable read. In addition to what you’ll find on these pages, we also offer a concise AI Crash Course, especially created with the needs of corporate leaders in mind. 5 | If you have any questions about AI or the work we do in the area, please contact: Claes Kaarni Vice President [email protected] +358 50 487 7255 Katja Metsola Head of Sales, Data Science [email protected] +358 40 745 3770 | 6 Contents 09 - How do machines learn? (Antti Ajanki) 19 - Differences between Machine Learning and software engineering (Antti Ajanki) 27 - Fantastic Problems and Where to Find Them (Daryl Weir) 45 - Six inspirational ways to make money with data (Mika Ruokonen) 57 - AI Glossary 7 | | 8 How do machines learn? Antti Ajanki - Data Scientist A brief and easy-to-understand look at what Machine Learning is, how a machine learns and what sorts of tasks it’s suited for. Recommended for anyone interested in, but not conversant in the basics of Machine Learning 9 | | 10 Machine Learning is the secret sauce behind speech recognition, self-driving cars and other fascinating technologies. But what exactly is Machine Learning, and where can it be applied? Learning by choosing an algorithm The only thing that computers (that is the “machine” part of Machine Learning) do is execute algorithms. An algorithm is a fixed sequence of steps. Computers aren’t creative, nor can they come up with new ideas. How do they then learn anything at all? For any given task there are countless conceivable algorithms, some better and some worse. If a computer is instructed to evalu- ate algorithms one by one and keep track of the best algorithm it has encountered so far, its performance in the task will increase over time as it stumbles upon better algorithms. It seems as if the computer is gradually learning to master the task. This is the core idea behind Machine Learning. Let’s consider a spam filter, which was an early success case for Machine Learning. A spam filter takes an email message as an input, processes it (somehow), and finally outputs a label: “spam” or “not spam”. One could try to write a spam filtering algorithm by hand. Perhaps collect a list of spammy words (“casino”, “free”, “genuine”, etc.), count how many of these words appear in an email message, and say that a message is spam if the count exceed a certain threshold. However, there is no straightforward criterion 11 | of deciding which words should be on the list (“free” is likely to appear in a legitimate message, too) or the threshold value. Machine Learning, on the other hand, can diligently evaluate millions of word list variants and pick the one that most accurate- ly detects spam. Data as a yardstick for comparing algorithms So far we have seen that Machine Learning boils down to a selection of a well-performing algorithm. But how do we measure the performance? A fair way to compare algorithms is to evaluate them on a data set representative of the task the computer is supposed to learn. Typically, this training data is a set of input ex- amples and corresponding human-annotated expected outputs. For example, a set of email messages that a person has read and annotated either as “spam” or “not spam” would work as training data for learning a spam filter. Candidate algorithms are ranked based on how accurately they replicate the expected outputs when they are fed input data instances. Of course, ultimately we are interested on how well an algorithm will perform on inputs which are not part of the training (after all, we already know the correct output for all the training instances). If the training data is representative and the evaluation is done carefully, there are guarantees that the performance generalizes to previously unseen data points. | 12 Choosing an algorithm: an exercise in mathematical optimization Where do the candidate algorithms come from? A data scientist, who is setting up the learning task, has to decide which algorithms the computer should consider. The choice of which algorithms to evaluate is affected by two, of- ten contradictory, considerations: the set of candidate algorithms should be flexible enough to capture essential features of the task (such as information about which variables depend on each other), and, at the same time, the search over the candidates should be mathematically tractable. Often, the candidate set is selected so that a comparison between algorithms reduces to a mathematical optimization problem. Optimization is a heavily studied field, and plenty of tools exists for solving optimization problems effectively. Example: learning a spam filter The image below visualizes the task of learning a simple spam filter. It depicts emails that are collected for training purposes and three instances of competing algorithms. The training emails are shown as dots. The solid dots are spam and the open dots are non-spam messages. In this example, emails are described by just two variables that are assumed to be related to their degree of spamminess: the number of words in an email (vertical axis) and the number of exclamation mark in an email (horizontal). 13 | E-MAIL LENGTH NUMBER OF EXLCAMATION MARKS I have decided to restrict the search space of algorithms to lines. A line defines a spam filter: emails that fall on one side of the line are classified as spam and the ones on the other side as non- spam. The three pictures show three examples out of infinitely many possible ways of drawing a line on the plane. Machine Learning now becomes optimization: choose a line a line so that as many of the solid dots as possible fall on the one side of the line and as many open dots as possible on the other side. Obviously, the rightmost picture is the optimal solution, and hence the most accurate spam filter, because it cleanly separates the two types of dots. Of course, beyond this simplified example, there are usually more than two variables, and the data might not separate so cleanly into two regions. These introduce some additional challenges, but the basic principles of learning stay the same. | 14 Where Machine Learning can help? Some tasks are more suited for Machine Learning that others. Machine Learning depends on the availability of training data. Therefore, tasks with many easily available training instances are particularly suitable for Machine Learning.